<p>Rainfall intensity represents one of the most critical climatological parameters, widely utilized in water resources engineering for diverse planning and design applications, as well as for flood and drought management studies. Under current scenario, rainfall simulation utilizing the stochastic models has proved to be an indispensable tool. In this respect, Markov chain models involving a two stage approach, rainfall occurrence and amount generation have evolved and gained prominence. The present study primarily evolves around the systematic application and subsequent performance evaluation of the prevalent Two state and Three state variants of Markov chain encompassing higher orders across 58 stations spanning within India. This analysis facilitates the identification of the most relevant Markov chain state-order variant for each station from hydrological aspect which will eventually help in simulation of realistic daily rainfall sequences. Additionally, the study explores the relevance of a Hybrid Three-state Markov chain model, which requires the estimation of only three transition probabilities, thereby offering a computationally efficient alternative to the conventional Three-state model that demands six, thereby addressing the issue of parameter parsimony. Moreover, the study evaluates whether model suitability can be generalized across regions based on Köppen climate classifications, thereby providing a systematic framework for selecting optimal Markov model structures under varying climatic contexts. Based on a suite of statistical performance measures, the robustness and applicability of the Hybrid state model as a potential unified and reliable variant for rainfall simulation across entire India has also been presented. By integrating information-theoretic model selection with hydrologically relevant performance diagnostics, this study provides a comprehensive assessment of trade-offs between statistical parsimony and physical realism in Markov-based rainfall simulation.</p>